---
title: "Admission Dashboard"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    orientation: columns
    vertical_layout: fill
    social: ["twitter", "linkedin"]
    source_code: embed
    theme: bootstrap
    logo: static/logo.png
    favicon: static/favicon.png
    css: style.css
---

```{r setup, include=FALSE}
gc()
rm(list = ls())

if (!require("pacman")) install.packages("pacman")
pacman::p_load(
  tidyverse, flexdashboard,
  here, styler,
  hrbrthemes, ggthemes, ggtext, plotly,
  glue, waffle, DT, geofacet
)

dat <- read_csv(here("data/data-cleaned.csv"))

style_file("index.Rmd")
```

Insights {.storyboard data-icon="fa-chart-line" data-commentary-width=200}
===================================== 


### **Overall admission results.** Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

```{r, fig.width=8, fig.height=8}
dat |>
  count(decision) -> admission_summary

p1 <- ggplot(admission_summary, aes(fill = decision, values = n)) +
  geom_waffle(color = "white", size = 1.125, n_rows = 9, flip = TRUE) +
  scale_fill_manual(
    name = NULL,
    values = c("#1A6899", "#FFCF58", "#FC5449"),
    labels = c("Admit", "Waitlist", "Decline")
  ) +
  coord_equal() +
  theme_ipsum_rc() +
  theme(
    legend.position = "bottom",
    legend.title = element_text(color = "#000000"),
    legend.text = element_text(color = "#000000"),
    axis.title.y = element_text(
      family = "IBM Plex Sans", face = "bold"
    ),
    axis.title.x = element_text(
      family = "IBM Plex Sans", face = "bold"
    ),
    axis.text.x = element_blank(),
    axis.text.y = element_blank(),
    text = element_text(
      family = "IBM Plex Sans",
      color = "#3B372E"
    ),
    plot.title = element_text(hjust = 0.5),
    plot.subtitle = element_markdown(hjust = 0.5),
    plot.caption = element_markdown(),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()
  ) +
  labs(
    title = "SUMMER 2022 ADMISSION RESULTS",
    subtitle = "BAD DATA EXCLUDED.",
    caption = glue("SOURCE: SUMMERSTUDENTADMISSION2.CSV"),
    x = "",
    y = ""
  )

p1
```

***

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.


### **Applicants by states.**

```{r, fig.width=8,fig.height=8}

# standardize all numeric variables

dat_stand <- dat %>%
  mutate(
    decision = as_factor(decision),
    state = as_factor(state),
    gender = as_factor(gender),
    across(where(is.numeric), ~ round(scale(.)[, 1], 2))
  )

dat_stand_long <- dat_stand %>% pivot_longer(
  cols = c(
    gpa, work_exp, test_score,
    writing_score, volunteer_level
  ),
  names_to = "variable",
  values_to = "value"
)

# dat_stand_long %>%
#   ggplot(aes(x = variable, y=value,
#              fill = variable)) +
#   geom_col() +
#   coord_flip() +
#   theme_ipsum_rc() +
#   facet_geo(~ state, grid = "us_state_grid2")
```

*** 

This might not be a good idea since the applicants only come from a few states, the geofacet viz would be VERY sparse.

### **Gender (in)equality.**

```{r, fig.width=8,fig.height=8}

```

*** 

In fact there admission stats by gender is nothing special:

- male 44, admit 15 + decline 16 + waitlist 13
- female 34, admit 12 + decline 12 + waitlist 10


Actionable Decisions {.storyboard data-icon="fa-graduation-cap"}
===================================== 


### **Decision/conclusion A**

```{r, fig.width=8,fig.height=8}

```

*** 

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

### **Decision/conclusion B**

```{r, fig.width=8,fig.height=8}

```

*** 

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

### **Decision/conclusion C**

```{r, fig.width=8,fig.height=8}

```

*** 

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.


About {data-icon="fa-info" data-orientation=columns}
=====================================

Column {data-width=350}
-----------------------------------------------------------------------

The dataset contains 81 valid admission results from the CSV file `SummerStudentAdmissions2.csv`.

Three versions of this dataset are included on the right hand side:

- Standardized data.
- Cleaned data.
- Raw data.

Due to the lack of information, some of the variables and contents from the dataset are interpreted intuitively.

In the cleaned dataset,

- `gender=-1` means the gender is undisclosed.
- `volunteer_level` is ranked from 5 to 0.
- `gpa` is calculated on a 4.0 scale.
- `writing_score` should be on a 100 scale.
- `test_score` has rather limited information.
- `work_exp`'s unit is year.

***

The dashboard is powered by

- [`flexdashboard`](https://pkgs.rstudio.com/flexdashboard/)
- [`DT`](https://rstudio.github.io/DT/)
- [`plotly`](https://plotly.com/)
- The static visualization theme is customized based on [`hrbrmstr`](https://github.com/hrbrmstr/hrbrthemes).


***

The codes are open-sourced. **Please feel free to star or fork this repository.**


Star
Fork





Column {.tabset data-width=650 data-height=1000}
-----------------------------------------------------------------------

### Standardized data

```{r}
DT::datatable(dat_stand,
  options = list(
    bPaginate = FALSE
  ),
  style = "bootstrap"
) %>%
  formatStyle(
    "decision",
    backgroundColor = styleEqual(
      c("Admit", "Decline", "Waitlist"),
      c("#1A6899", "#FC5449", "#FFCF58")
    )
  ) %>%
  formatStyle(c(
    "gpa", "work_exp", "test_score", "writing_score",
    "volunteer_level"
  ),
  background = styleColorBar(range(c(
    dat_stand$gpa, dat_stand$work_exp, dat_stand$test_score, dat_stand$writing_score,
    dat_stand$volunteer_level
  )), "lightblue"),
  backgroundSize = "98% 88%",
  backgroundRepeat = "no-repeat",
  backgroundPosition = "center"
  )
```

### Cleaned data

```{r}
dat <- read_csv("data/data-cleaned.csv")

DT::datatable(dat,
  options = list(
    bPaginate = FALSE
  ),
  style = "bootstrap"
) %>%
  formatStyle(
    "decision",
    backgroundColor = styleEqual(
      c("Admit", "Decline", "Waitlist"),
      c("#1A6899", "#FC5449", "#FFCF58")
    )
  )
```

### Raw data

```{r}
DT::datatable(read_csv("data/SummerStudentAdmissions2.csv"),
  options = list(
    bPaginate = FALSE
  ),
  style = "bootstrap"
)
```